Multiple objective scheduling of HPC workloads through dynamic prioritization

We have developed an efficient single queue scheduling system that utilizes a greedy knapsack algorithm with dynamic job priorities. Our scheduler satisfies high level objectives while maintaining high utilization of the HPC system or collection of distributed resources such as a computational GRID. We provide simulation analysis of our approach in contrast with various scheduling strategies of shortest job first; longest waiting jobs first; and large jobs first. Further, we look at the effects of system size on the total workload response time and find that for real workloads, the relationship between response time and system size follows an inverse power law. Our approach does not require system administrators or users to identify a specific priority queue for each of their jobs. The proposed scheduler performs an exhaustive parameter search for a priority calculation per job to balance high level objectives and provide guaranteed performance jobs in a workload. The system administrator needs only tune the prioritization parameters (knobs) and the system scheduler will behave accordingly, such as reducing wait time for jobs that are above average size with small runtimes. We demonstrate that our approach works very well on workloads that have many independent tasks. We evaluate our scheduler on a realistic mixed scientific data processing workload and with a realistic HPC workload trace from the parallel workloads archive.